plot_video_api.py

"""
=========
Video API
=========

.. note::
    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_video_api.ipynb>`_
    or :ref:`go to the end <sphx_glr_download_auto_examples_others_plot_video_api.py>` to download the full example code.

This example illustrates some of the APIs that torchvision offers for
videos, together with the examples on how to build datasets and more.
"""

# %%
# 1. Introduction: building a new video object and examining the properties
# -------------------------------------------------------------------------
# First we select a video to test the object out. For the sake of argument
# we're using one from kinetics400 dataset.
# To create it, we need to define the path and the stream we want to use.

# %%
# Chosen video statistics:
#
# - WUzgd7C1pWA.mp4
#     - source:
#         - kinetics-400
#     - video:
#         - H-264
#         - MPEG-4 AVC (part 10) (avc1)
#         - fps: 29.97
#     - audio:
#         - MPEG AAC audio (mp4a)
#         - sample rate: 48K Hz
#

import torch
import torchvision
from torchvision.datasets.utils import download_url
torchvision.set_video_backend("video_reader")

# Download the sample video
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/WUzgd7C1pWA.mp4?raw=true",
    ".",
    "WUzgd7C1pWA.mp4"
)
video_path = "./WUzgd7C1pWA.mp4"

# %%
# Streams are defined in a similar fashion as torch devices. We encode them as strings in a form
# of ``stream_type:stream_id`` where ``stream_type`` is a string and ``stream_id`` a long int.
# The constructor accepts passing a ``stream_type`` only, in which case the stream is auto-discovered.
# Firstly, let's get the metadata for our particular video:

stream = "video"
video = torchvision.io.VideoReader(video_path, stream)
video.get_metadata()

# %%
# Here we can see that video has two streams - a video and an audio stream.
# Currently available stream types include ['video', 'audio'].
# Each descriptor consists of two parts: stream type (e.g. 'video') and a unique stream id
# (which are determined by video encoding).
# In this way, if the video container contains multiple streams of the same type,
# users can access the one they want.
# If only stream type is passed, the decoder auto-detects first stream of that type and returns it.

# %%
# Let's read all the frames from the video stream. By default, the return value of
# ``next(video_reader)`` is a dict containing the following fields.
#
# The return fields are:
#
# - ``data``: containing a torch.tensor
# - ``pts``: containing a float timestamp of this particular frame

metadata = video.get_metadata()
video.set_current_stream("audio")

frames = []  # we are going to save the frames here.
ptss = []  # pts is a presentation timestamp in seconds (float) of each frame
for frame in video:
    frames.append(frame['data'])
    ptss.append(frame['pts'])

print("PTS for first five frames ", ptss[:5])
print("Total number of frames: ", len(frames))
approx_nf = metadata['audio']['duration'][0] * metadata['audio']['framerate'][0]
print("Approx total number of datapoints we can expect: ", approx_nf)
print("Read data size: ", frames[0].size(0) * len(frames))

# %%
# But what if we only want to read certain time segment of the video?
# That can be done easily using the combination of our ``seek`` function, and the fact that each call
# to next returns the presentation timestamp of the returned frame in seconds.
#
# Given that our implementation relies on python iterators,
# we can leverage itertools to simplify the process and make it more pythonic.
#
# For example, if we wanted to read ten frames from second second:


import itertools
video.set_current_stream("video")

frames = []  # we are going to save the frames here.

# We seek into a second second of the video and use islice to get 10 frames since
for frame, pts in itertools.islice(video.seek(2), 10):
    frames.append(frame)

print("Total number of frames: ", len(frames))

# %%
# Or if we wanted to read from 2nd to 5th second,
# We seek into a second second of the video,
# then we utilize the itertools takewhile to get the
# correct number of frames:

video.set_current_stream("video")
frames = []  # we are going to save the frames here.
video = video.seek(2)

for frame in itertools.takewhile(lambda x: x['pts'] <= 5, video):
    frames.append(frame['data'])

print("Total number of frames: ", len(frames))
approx_nf = (5 - 2) * video.get_metadata()['video']['fps'][0]
print("We can expect approx: ", approx_nf)
print("Tensor size: ", frames[0].size())

# %%
# 2. Building a sample read_video function
# ----------------------------------------------------------------------------------------
# We can utilize the methods above to build the read video function that follows
# the same API to the existing ``read_video`` function.


def example_read_video(video_object, start=0, end=None, read_video=True, read_audio=True):
    if end is None:
        end = float("inf")
    if end < start:
        raise ValueError(
            "end time should be larger than start time, got "
            f"start time={start} and end time={end}"
        )

    video_frames = torch.empty(0)
    video_pts = []
    if read_video:
        video_object.set_current_stream("video")
        frames = []
        for frame in itertools.takewhile(lambda x: x['pts'] <= end, video_object.seek(start)):
            frames.append(frame['data'])
            video_pts.append(frame['pts'])
        if len(frames) > 0:
            video_frames = torch.stack(frames, 0)

    audio_frames = torch.empty(0)
    audio_pts = []
    if read_audio:
        video_object.set_current_stream("audio")
        frames = []
        for frame in itertools.takewhile(lambda x: x['pts'] <= end, video_object.seek(start)):
            frames.append(frame['data'])
            audio_pts.append(frame['pts'])
        if len(frames) > 0:
            audio_frames = torch.cat(frames, 0)

    return video_frames, audio_frames, (video_pts, audio_pts), video_object.get_metadata()


# Total number of frames should be 327 for video and 523264 datapoints for audio
vf, af, info, meta = example_read_video(video)
print(vf.size(), af.size())

# %%
# 3. Building an example randomly sampled dataset (can be applied to training dataset of kinetics400)
# -------------------------------------------------------------------------------------------------------
# Cool, so now we can use the same principle to make the sample dataset.
# We suggest trying out iterable dataset for this purpose.
# Here, we are going to build an example dataset that reads randomly selected 10 frames of video.

# %%
# Make sample dataset
import os
os.makedirs("./dataset", exist_ok=True)
os.makedirs("./dataset/1", exist_ok=True)
os.makedirs("./dataset/2", exist_ok=True)

# %%
# Download the videos
from torchvision.datasets.utils import download_url
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/WUzgd7C1pWA.mp4?raw=true",
    "./dataset/1", "WUzgd7C1pWA.mp4"
)
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/RATRACE_wave_f_nm_np1_fr_goo_37.avi?raw=true",
    "./dataset/1",
    "RATRACE_wave_f_nm_np1_fr_goo_37.avi"
)
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/SOX5yA1l24A.mp4?raw=true",
    "./dataset/2",
    "SOX5yA1l24A.mp4"
)
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/v_SoccerJuggling_g23_c01.avi?raw=true",
    "./dataset/2",
    "v_SoccerJuggling_g23_c01.avi"
)
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/v_SoccerJuggling_g24_c01.avi?raw=true",
    "./dataset/2",
    "v_SoccerJuggling_g24_c01.avi"
)

# %%
# Housekeeping and utilities
import os
import random

from torchvision.datasets.folder import make_dataset
from torchvision import transforms as t


def _find_classes(dir):
    classes = [d.name for d in os.scandir(dir) if d.is_dir()]
    classes.sort()
    class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
    return classes, class_to_idx


def get_samples(root, extensions=(".mp4", ".avi")):
    _, class_to_idx = _find_classes(root)
    return make_dataset(root, class_to_idx, extensions=extensions)

# %%
# We are going to define the dataset and some basic arguments.
# We assume the structure of the FolderDataset, and add the following parameters:
#
# - ``clip_len``: length of a clip in frames
# - ``frame_transform``: transform for every frame individually
# - ``video_transform``: transform on a video sequence
#
# .. note::
#   We actually add epoch size as using :func:`~torch.utils.data.IterableDataset`
#   class allows us to naturally oversample clips or images from each video if needed.


class RandomDataset(torch.utils.data.IterableDataset):
    def __init__(self, root, epoch_size=None, frame_transform=None, video_transform=None, clip_len=16):
        super(RandomDataset).__init__()

        self.samples = get_samples(root)

        # Allow for temporal jittering
        if epoch_size is None:
            epoch_size = len(self.samples)
        self.epoch_size = epoch_size

        self.clip_len = clip_len
        self.frame_transform = frame_transform
        self.video_transform = video_transform

    def __iter__(self):
        for i in range(self.epoch_size):
            # Get random sample
            path, target = random.choice(self.samples)
            # Get video object
            vid = torchvision.io.VideoReader(path, "video")
            metadata = vid.get_metadata()
            video_frames = []  # video frame buffer

            # Seek and return frames
            max_seek = metadata["video"]['duration'][0] - (self.clip_len / metadata["video"]['fps'][0])
            start = random.uniform(0., max_seek)
            for frame in itertools.islice(vid.seek(start), self.clip_len):
                video_frames.append(self.frame_transform(frame['data']))
                current_pts = frame['pts']
            # Stack it into a tensor
            video = torch.stack(video_frames, 0)
            if self.video_transform:
                video = self.video_transform(video)
            output = {
                'path': path,
                'video': video,
                'target': target,
                'start': start,
                'end': current_pts}
            yield output

# %%
# Given a path of videos in a folder structure, i.e:
#
# - dataset
#     - class 1
#         - file 0
#         - file 1
#         - ...
#     - class 2
#         - file 0
#         - file 1
#         - ...
#     - ...
#
# We can generate a dataloader and test the dataset.


transforms = [t.Resize((112, 112))]
frame_transform = t.Compose(transforms)

dataset = RandomDataset("./dataset", epoch_size=None, frame_transform=frame_transform)

# %%
from torch.utils.data import DataLoader
loader = DataLoader(dataset, batch_size=12)
data = {"video": [], 'start': [], 'end': [], 'tensorsize': []}
for batch in loader:
    for i in range(len(batch['path'])):
        data['video'].append(batch['path'][i])
        data['start'].append(batch['start'][i].item())
        data['end'].append(batch['end'][i].item())
        data['tensorsize'].append(batch['video'][i].size())
print(data)

# %%
# 4. Data Visualization
# ----------------------------------
# Example of visualized video

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 12))
for i in range(16):
    plt.subplot(4, 4, i + 1)
    plt.imshow(batch["video"][0, i, ...].permute(1, 2, 0))
    plt.axis("off")

# %%
# Cleanup the video and dataset:
import os
import shutil
os.remove("./WUzgd7C1pWA.mp4")
shutil.rmtree("./dataset")